Preictal connectivity dynamics: Exploring inflow and outflow in iEEG networks

Focal resective surgery can be an effective treatment option for patients with refractory epilepsy if the seizure onset zone is accurately identied through intracranial EEG recordings. The traditional concept of the epileptogenic zone has expanded to the notion of an epileptogenic network, emphasizing the role of interconnected brain regions in seizure generation. Precise delineation of this network is essential for optimizing surgical outcomes. Over the past 3 decades, several quantitative connectivity methods have been developed to study the interactions between the seizure onset zone and non-involved regions. Despite these advances, the mechanisms governing the transition from interictal to ictal periods remain poorly understood. In this study, we investigated preictal interactions between the seizure onset zone and the broader network using directed connectivity measures.

We evaluated their effectiveness in identifying seizure onset zones using a multicenter intracranial EEG dataset, encompassing 243 seizures from 61 patients. Directed transfer function and partial directed coherence were used to extract connectivity matrices during 28-seconds of preictal period in patients with good surgery outcomes. Inflow and outflow metrics were computed for iEEG electrode contact annotated as seizure onset zone and the performance of each metric is assessed in disentangling these electrodes from the rest of the network.

We observed two distinct patterns of network connectivity preceding seizure onset. While there was an increase in inflow of information to seizure onset electrodes in one subset of patients, in the other subset, there was a prominent outflow of information from seizure onset electrodes to the rest of the network, suggesting distinct connectivity patterns associated with the seizure onset zone across patients. Further analyses showed that patients who underwent the grid/strip/depth implantation approach exhibited significantly higher area under curve (AUC) than those with electrocorticography (ECoG) or stereo-electroencephalography (sEEG) alone. Finally, examining the influence of lesional vs non-lesional neuroimaging status demonstrated that a greater proportion of high-inflow and high-outflow were lesional.

Our findings reinforce the notion that seizure generation is driven by dynamic shifts in information flow within the brain's functional network. The preictal connectivity patterns observed --either increased inflow to the seizure onset zone or high outflow from it --underscore the network reorganization involved in epileptic transitions. These results emphasize epilepsy as a network-level phenomenon, supporting the use of network concepts for better understanding seizure dynamics and improving surgical localization strategies.